A Reinforced k-Nearest Neighbors Method With Application to Chatter Identification in High-Speed Milling

2020 ◽  
Vol 67 (12) ◽  
pp. 10844-10855
Author(s):  
Fei Shi ◽  
Hongrui Cao ◽  
Xingwu Zhang ◽  
Xuefeng Chen
Keyword(s):  
High Speed ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
High Speed Milling ◽  
Chatter Identification

scholarly journals Errors classification method for electric motor torque measurement

2021 ◽  
Vol 4 (1(60)) ◽  
pp. 42-48
Author(s):  
Mykola Kulyk ◽  
Volodymyr Kvasnikov ◽  
Dmytro Kvashuk ◽  
Anatolii Beridze-Stakhovskyi
Keyword(s):  
Electric Motor ◽  
Strain Gauge ◽  
High Speed ◽  
Supply Voltage ◽  
Frequency Converter ◽  
Influence Factors ◽  
Nearest Neighbors ◽  
Measuring Instruments ◽  
Maximum Deviation ◽  
K Nearest Neighbors

The use of high-precision measuring instruments for determining the torque of electric motors in such areas as medicine, motor transport, shipping, aviation requires the improvement of the metrological characteristics of measuring instruments. This, in turn, requires an accurate assessment of their error. Of particular importance is the measurement of power at high-speed installations, where in some cases conventional measurement systems are either unsuitable or have low accuracy. Thus, the use of high-speed turbomachines in aviation, transport, and rocketry creates an urgent need for the development of high-quality measuring instruments for conducting precise research. In turn, in the absence of means for accurately determining the error, attempts are made to predict them. This makes it possible to timely identify the influence of many factors on the accuracy of measuring instruments. The increase in the error arises, as a rule, through abrupt changes in the measurement conditions. Such errors are unpredictable, and their significance is difficult to predict. In the course of the study, the K-nearest neighbors method was used, to establish criteria for which a gross error may occur. The results obtained make it possible to establish threshold values at which the maximum deviation can be established under various conditions of the experiment. In a computational experiment using the K-nearest neighbors method, the following factors were investigated: vibration; temperature rise of measuring sensors; instabilities in the supply voltage of the electric motor, which affect the accuracy of the strain gauge and frequency converter. As a result, the maximum errors were obtained depending on the indicated influence factors. It has been experimentally confirmed that the K-nearest neighbors method can be used to classify deviations of the nominal value of the error of measuring instruments under various measurement conditions. A metrological stand has been developed for the experiment. It includes a strain gauge sensor for measuring torque and a photosensitive sensor for measuring the speed of the electric motor. Signal conversion from these sensors is implemented on the basis of the ESP8266 microcontroller

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Location Privacy ◽  
Nearest Neighbors ◽  
Search Pattern ◽  
Broadcast Encryption ◽  
Mobile Social Networks ◽  
K Nearest Neighbors ◽  
Fine Grained ◽  
Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

scholarly journals A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2940
Author(s):  
Luciano Ortenzi ◽  
Simone Figorilli ◽  
Corrado Costa ◽  
Federico Pallottino ◽  
Simona Violino ◽  
...  
Keyword(s):  
Machine Vision ◽  
Nearest Neighbors ◽  
Calibration Method ◽  
Mobile App ◽  
Harvest Time ◽  
Automatic Assessment ◽  
K Nearest Neighbors ◽  
Olive Ripeness ◽  
Olive Ripening

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

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